Cohere Transcribe is a 2-billion-parameter multilingual Automatic Speech Recognition (ASR) model released by Cohere on March 26, 2026. Based on the Conformer architecture, which jointly processes temporal features and context of speech, it converts audio in 14 languages—including Korean, English, Japanese, and Chinese—into text. Similar to how GPT-series models handle text from multiple languages within a single interface, Cohere Transcribe functions as a tool that treats audio recorded in different languages through a unified multilingual transcription model. The model has been made publicly available for execution not only via the Cohere API but also on local GPUs or custom infrastructure, with its application under the Apache-2.0 license facilitating independent deployment reviews by enterprises and research institutions. Existing speech transcription workflows often require maintaining separate models for each language or transmitting raw audio data to external APIs. The former complicates model selection and operational configuration, while the latter increases data governance burdens when handling sensitive voice data such as clinical interviews, undisclosed research presentations, or internal meetings. Cohere Transcribe differentiates itself as a multilingual ASR model designed with enterprise deployment and local execution in mind. Researchers can operate the same model on local GPUs or institutional infrastructure, choosing to use the Cohere API as needed. However, details regarding supported audio formats, maximum input length, speaker diarization support, timestamping, and streaming transcription capabilities cannot be confirmed solely from the provided Discovery information and require verification through official documentation. In life sciences research, it can be utilized to convert multilingual interviews or clinical study recordings into searchable text resources. For example, patient interviews or research participant interviews conducted in Korean and English can be transcribed within an institutional GPU environment, followed by the removal of names, institutions, and contact information using de-identification tools, allowing symptoms and treatment experiences to be coded in qualitative research software. The ability to opt for configurations that do not send raw audio to external APIs offers practical advantages for studies requiring control over the storage location and access paths of sensitive data. Prior to actual implementation, however, it is necessary to separately review the institution’s privacy standards and clinical data processing regulations. Additionally, it can be used to convert seminar recordings, experimental protocol explanations, and field survey audio from international collaborative research into text by language, which can then be connected to translation models or search systems. Transcribing research audio in multiple languages using a single model reduces the burden of managing separate ASR pipelines for each language and allows transcription results to serve as common inputs for document search, summarization, and keyword extraction. However, quantitative metrics such as per-language accuracy, recognition rates for specialized life science terminology, and performance in noisy environments are not included in the current input information; therefore, Word Error Rate comparisons using representative audio samples are required prior to actual deployment.

Cohere Transcribe is a 2-billion-parameter multilingual automatic speech recognition (ASR) model released by Cohere on March 26, 2026. It is based on the Conformer architecture, which jointly processes temporal features and context of speech, and transcribes speech in 14 languages, including Korean, English, Japanese, and Chinese. Similar to how GPT-series models handle text from multiple languages within a single interface, Cohere Transcribe handles audio recorded in different languages through a single multilingual transcription model.

🔬 Multilingual Clinical Interview Transcription: Transcribe Korean and English patient interview recordings on internal institutional GPUs, linking personal information de-identification with qualitative coding. Language-specific accuracy is validated using the Word Error Rate of representative samples, then applied to analyze symptoms, adverse effects, and treatment experiences.